Graph-enabled customer intelligence in a post-cookie world

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Neo4j’s Alicia Frame explains how retail brands that once relied on third-party cookie data will need to shift their strategy to measuring other downstream metrics using smart data technology

Frame: graph databases can be applied to achieve consumer insight and loyalty

For years, brands have relied on third-party cookies to collect user data, track website visitors, target ads to specific audiences, and improve the user experience. However, this highly useful functionality is about to be removed, and advertisers have a year to adjust.

This could present a problem, as retailers have ended up relying on the approach. Delivering real-time recommendations to online shoppers is a proven way to maximise revenue to improve customer experience and sales. Shoppers now expect finely tuned recommendations, and react poorly to one-size-fits-all or uninformed recommendations (e.g., “I’ve already bought that. Why are they showing it to me again?”). To be effective, recommendations must be personalised based on the individual consumer’s preferences, shopping history, interests, and needs. Cookie-based profiles mean brands can deliver personalised real-time recommendations in order to maximise the value they deliver, secure customer loyalty, and sell effectively.

Instantly capture any new interests shown 

The demise of the third-party cookie is a good time for brands to look at graph database technology. By design, graph databases are able to quickly query customers’ past purchases, and instantly capture any new interests shown in the current online visit. Both are essential for making real-time recommendations. 

To be clear, graph databases are a data technology, not a cookie substitute, but they can be applied to achieve consumer insight and loyalty. After all, they are a well-established technology for enabling real-time recommendations. They are helping brands understand their online shoppers’ behaviour and are the basis for a perfect tool for real-time product recommendations. And the good news is that graph database technology are being used to build high-functioning recommendation engines that don’t rely on third-party cookies. Such recommendation engines make use of web logs to offer the customer the value and meaning that brands rely on—and arguably more. And their use to enhance brands’ marketing tactics could greatly rise in the next couple of years as we move away from cookies.

Millions of views and millions of unique visits per month

US-based Meredith Corporation, a $3bn media conglomerate working for major US consumer brands with digital presences that reach 180 million-plus users a month, is generating customer insight using graph technology rather than third-party tracking data to build unique customer profiles. The firm’s Senior Data Scientist Ben Squire explains, “With millions of views and millions of unique visits per month across different topics and lifestyles, our consumers trust us for information on things that affect their daily lives, as well as pique their interest. By understanding and analysing this content and how it’s consumed, we strive to serve the needs of our audiences and advertisers alike.”

And this is a company that’s changed up its data marketing due to cookie issues. Historically, Meredith identified anonymous users through third-party cookies. Still, even well before the Google announcement, cookie loss across diverse devices and ITP 2.3 browsers that block cookies by default increased the difficulty of relying on them, e.g., how IP addresses change constantly meant that the data could be unreliable. For Squire, “If the cookie ID used in the models doesn’t appear again, then the money, time and effort that goes into building those models is lost. Knowing your audience is not good enough; you need to see them again in order to act upon it.”

As a result, Meredith overhauled its approach. Using its rich mix of media content that generates multiple, disparate streams of data, its internal data scientists started to identify users across those streams. This pattern matching step revealed that cookies designed to identify unique users were repeated across different data streams. Squire and his team built a graph database based on more than 20 months of user data from first- and third-party sources. The new platform contains more than 4.4 terabytes of data across 30 billion nodes, 67 billion properties, and 35 billion relationships. 350 million profiles that would have been considered unique individuals with different interests and patterns have been consolidated into a smaller subset of 163 million denser and more precise profiles. 

Surviving the end of the third-party cookie

This consolidation has resulted in more relevant personalised content and advertising. By de-anonymising data at scale—in the hundreds of millions of users—and using graphs to find relationships between the cookie and other streams of data, the company claims it has completely transformed its understanding of customer behaviour. “By looking at how the data connects over time rather than just looking at individual cookies, we have increased our understanding of a customer by 20 to 30%,” Squire says. “As a result, instead of ‘advertising in the dark,’ we now better understand our customers—which translates into significant revenue gains and better-served ones.”

The call to action has to be that retailers and marketers need to ensure they survive the end of the third-party cookie. Graph technology could help them manage the web traffic and clickstream data they need to join the relationship ‘dots’ to get there.

The author is Director of Produce Management – Data Science at the world’s leading graph database, Neo4j